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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Manage memory, state, and execution | 10-15% | - Ensure continuity of agent memory and state across tools and environments
|
| Implement tool use and environment interaction | 20-25% | - Operate agents with safe execution paths and robust error handling
|
| Orchestrate multi-agent coordination | 15-20% | - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
|
| Implement guardrails and accountability | 10-15% | - Define autonomy levels
|
| Perform evaluation, error analysis, and tuning | 15-20% | - Define success criteria and evaluation signals for agent tasks
|
| Prepare agent architecture and SDLC processes | 15-20% | - Define boundaries between planning, reasoning, and action
|
Microsoft Developing in Agentic AI Systems Sample Questions:
You have multiple GitHub Copilot coding agents that run tasks concurrently.
You live stream the session log output and see the following.
* agent execution (ID 987654321)
Run ./scripts/agent-run.sh
--------------------------------
[agent] Starting task: Clean up infra configs
[agent] Executing step: Remove unused files
rm -rf /infra
Error: Command blocked by policy
Reason: destructive_operation_detected
[agent] Escalating to human review...
X Run agent task
X Process completed with exit code 1
X agent-execution failed
What is a possible cause of the error?
- A. a preToolUse hook
- B. a Copilot firewall policy
- C. a GitHub app installation policy
- D. a postToolUse hook
Correct Answer: A 🗳️
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?
- A. Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
- B. Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
- C. Copilot stores memories indefinitely until a repository administrator deletes them manually.
- D. Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.
Correct Answer: A 🗳️
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
You have a GitHub Copilot Enterprise subscription.
Developers use Microsoft Visual Studio Code.
You need to configure an array of subagents that can be invoked by an agent.
What should you configure in the YAML frontmatter for the agent?
- A. agents
- B. tools
- C. target
- D. mcp-servers
- E. handoffs
Correct Answer: A 🗳️
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
You have a GitHub repository that uses GitHub Actions to run an autonomous coding-agent workflow.
Your team requires that session logs be captured as workflow artifacts so that agent runs can be audited and traced back to a specific execution and code state.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.
Correct Answer:
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
agent-run that generates trace.json and plan.md
review that waits for human approval before continuing
deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
- A. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
- B. Use dependency caching to store trace.json and plan.md.
- C. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
- D. Commit trace.json and plan.md back to the repository from agent-run.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4SureReview members. You can sign-up / login (it's free).
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